Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to determining a candidate three-dimensional (3D) orientation of an object represented by a three-dimensional (3D) point cloud. The method includes receiving data indicative of a 3D point cloud comprising a plurality of 3D points, determining a first histogram for the plurality of 3D points based on geometric features determined based on the plurality of 3D points, accessing data indicative of a second histogram of geometric features of a 3D representation of a reference object, computing, for each of a plurality of different rotations between the first histogram and the second histogram in 3D space, a scoring metric for the associated rotation, and determining the candidate 3D orientation based on the scoring metrics of the plurality of different rotations.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to determining a two-dimensional (2D) profile of a portion of a three-dimensional (3D) point cloud. A 3D region of interest is determined that includes a width along a first axis, a height along a second axis, and a depth along a third axis. The 3D points within the 3D region of interest are represented as a set of 2D points based on coordinate values of the first and second axes. The 2D points are grouped into a plurality of 2D bins arranged along the first axis. For each 2D bin, a representative 2D position is determined based on the associated set of 2D points. Each of the representative 2D positions are connected to neighboring representative 2D positions to generate the 2D profile.

Abstract: The disclosure provides various components for a fishing lure. These components include a Hard Bodied Ballast Oscillator (“HBBO”) which oscillates around various axes, an HBB with positive lift that rises during retrieval, and a hook for use in a rigid body which, while being fished, remains at least partially inside the body and reliably pointing upwards, and which, when the fish bites, is at least partially exposed. In some cases multiple components may be connected to each other via a stiff wire and designed to maintain, in the water, a particular orientation of the components relative to each other.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model to image data. Image data of an object is received, the image data comprising a set of data entries. A set of regions of the image data are determined, wherein each region in the set of regions comprises an associated set of neighboring data entries in the set of data entries. Processed image data is generated, wherein the processed image data comprises a set of cells that each have an associated value, and generating the processed image data comprises, for each region in the set of regions, determining a maximum possible score of each data entry in the associated set of neighboring data entries from the image data, setting one or more values of the set of values based on the determined maximum possible score, and testing the pose of the model using the processed image data.

Abstract: Methods, systems, and devices for 3D measurement and/or pattern generation are provided in accordance with various embodiments. Some embodiments include a method of pattern projection that may include projecting one or more patterns. Each pattern from the one or more patterns may include an arrangement of three or more symbols that are arranged such that for each symbol in the arrangement, a degree of similarity between said symbol and a most proximal of the remaining symbols in the arrangement is less than a degree of similarity between said symbol and a most distal of the remaining symbols in the arrangement. Some embodiments further include: illuminating an object using the one or more projected patterns; collecting one or more images of the illuminated object; and/or computing one or more 3D locations of the illuminated object based on the one or more projected patterns and the one or more collected images.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to identify a surface feature of a portion of a three-dimensional (3D) point cloud. Data indicative of a path along a 3D point cloud is received, wherein the 3D point cloud comprises a plurality of 3D data points. A plurality of lists of 3D data points are generated, wherein: each list of 3D data points extends across the 3D point cloud at a location that intersects the received path; and each list of 3D data points intersects the received path at different locations. A characteristic associated with a surface feature is identified in at least some of the plurality of lists of 3D data points. The identified characteristics are grouped based on one or more properties of the identified characteristics. The surface feature is identified based on the grouped characteristics.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to determining a two-dimensional (2D) profile of a portion of a three-dimensional (3D) point cloud. A 3D region of interest is determined that includes a width along a first axis, a height along a second axis, and a depth along a third axis. The 3D points within the 3D region of interest are represented as a set of 2D points based on coordinate values of the first and second axes. The 2D points are grouped into a plurality of 2D bins arranged along the first axis. For each 2D bin, a representative 2D position is determined based on the associated set of 2D points. Each of the representative 2D positions are connected to neighboring representative 2D positions to generate the 2D profile.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model to image data. Image data of an object is received, the image data comprising a set of data entries. A set of regions of the image data are determined, wherein each region in the set of regions comprises an associated set of neighboring data entries in the set of data entries. Processed image data is generated, wherein the processed image data comprises a set of cells that each have an associated value, and generating the processed image data comprises, for each region in the set of regions, determining a maximum possible score of each data entry in the associated set of neighboring data entries from the image data, setting one or more values of the set of values based on the determined maximum possible score, and testing the pose of the model using the processed image data.

Abstract: Methods, systems, and devices for 3D measurement and/or pattern generation are provided in accordance with various embodiments. Some embodiments include a method of pattern projection that may include projecting one or more patterns. Each pattern from the one or more patterns may include an arrangement of three or more symbols that are arranged such that for each symbol in the arrangement, a degree of similarity between said symbol and a most proximal of the remaining symbols in the arrangement is less than a degree of similarity between said symbol and a most distal of the remaining symbols in the arrangement. Some embodiments further include: illuminating an object using the one or more projected patterns; collecting one or more images of the illuminated object; and/or computing one or more 3D locations of the illuminated object based on the one or more projected patterns and the one or more collected images.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model in three-dimensional data. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted to a field comprising a set of cells that each have an associated value, comprising determining, for each cell value, representative data based on one or more data entries from the set of data entries of the three-dimensional data. A pose of the model is tested with the field to determine a score for the pose.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model to image data. Image data of an object is received, the image data comprising a set of data entries. A set of regions of the image data are determined, wherein each region in the set of regions comprises an associated set of neighboring data entries in the set of data entries. Processed image data is generated, wherein the processed image data comprises a set of cells that each have an associated value, and generating the processed image data comprises, for each region in the set of regions, determining a maximum possible score of each data entry in the associated set of neighboring data entries from the image data, setting one or more values of the set of values based on the determined maximum possible score, and testing the pose of the model using the processed image data.

Abstract: Methods, systems, and devices for 3D measurement and/or pattern generation are provided in accordance with various embodiments. Some embodiments include a method of pattern projection that may include projecting one or more patterns. Each pattern from the one or more patterns may include an arrangement of three or more symbols that are arranged such that for each symbol in the arrangement, a degree of similarity between said symbol and a most proximal of the remaining symbols in the arrangement is less than a degree of similarity between said symbol and a most distal of the remaining symbols in the arrangement. Some embodiments further include: illuminating an object using the one or more projected patterns; collecting one or more images of the illuminated object; and/or computing one or more 3D locations of the illuminated object based on the one or more projected patterns and the one or more collected images.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model in three-dimensional data. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted to a field comprising a set of cells that each have an associated value, comprising determining, for each cell value, representative data based on one or more data entries from the set of data entries of the three-dimensional data. A pose of the model is tested with the field to determine a score for the pose.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model to image data. Image data of an object is received, the image data comprising a set of data entries. A set of regions of the image data are determined, wherein each region in the set of regions comprises an associated set of neighboring data entries in the set of data entries. Processed image data is generated, wherein the processed image data comprises a set of cells that each have an associated value, and generating the processed image data comprises, for each region in the set of regions, determining a maximum possible score of each data entry in the associated set of neighboring data entries from the image data, setting one or more values of the set of values based on the determined maximum possible score, and testing the pose of the model using the processed image data.

Abstract: The present disclosure provides a multi-stage image mapping mechanism for mapping a distorted image to a rectified image. For example, the multi-stage image mapping mechanism can remove homography from a distorted image to reconstruct a rectified image in two-stages: (1) a first stage in which distortion is partially removed from a distorted image to generate an intermediate image, and (2) a second stage in which residual distortion is removed from the intermediate image to recover the rectified image.